1. Field of the Invention
The present invention generally relates to computer-aided detection (CAD) of suspicious regions in three-dimensional medical imagery and, in particular, relates to improving shape estimates of nodules detected in three-dimensional imagery of lungs as well as registering three-dimensional imagery from multiple computed tomography (CT) exams.
2. Discussion of Background
Lung cancer is now the most common form of cancer diagnosed in the United States and remains a leading cause of cancer death in the United States in both males and females. Lung cancer accounts for 14% of all cancer diagnoses and over 30% of all cancer deaths. The American Cancer Society estimates approximately 173,000 new lung cancer cases will be detected and some 160,000 lives will be lost in 2004. The death rate from lung cancer rose 600 percent between 1930 and 1997, according to a report from the Journal of the American Medical Association.
Lung cancer is caused by abnormal cells growing within the lung tissue and has a risk of spreading to lymph nodes in the lungs and mediastinum. The earlier lung cancer is detected, the better the chance the patient's survival rate will increase. According to the American Cancer Society, the overall five-year survival rate for lung cancer is less than 15 percent. However, when lung cancer is found in the early stages (Stage 1), the five-year survival rate increases to more than 50 percent. Unfortunately, lung cancer is difficult to detect in its early stages. Today, only 15 percent of lung cancer is detected in the early, most treatable stages.
The chest radiograph, long the mainstay of radiology, often provides the first opportunity for a radiologist to diagnose a patient with lung cancer. Conventional chest x-rays typically provide two images for a radiologist to review. The images show front and side views of a patient's chest. A complicated anatomy combined with perceptual problems that accompany the projection of a three-dimensional object (the patient) into two dimensions (the image plane), however, makes identification of lung nodules a burdensome task for radiologists, resulting in a detection rate has been estimated to be between 20% and 50%, see Lorentz, et al., “Miss rate of lung cancer on the chest radiograph in clinical practice,” Chest, 115:720-724, 1999. Detected nodules are usually large and at a later stage. Computer vision methods for assisting interpretation of chest x-rays have been researched and developed over at least the past 16 years. See, for example, U.S. Pat. Nos. 4,851,984 and No. 4,839,807.
CT systems produce volumetric images, providing three-dimensional information of structures internal to the body. The detection of lung nodules in CT systems is still confounded by the presence of blood vessels in the lungs. In addition, this imagery is commonly viewed on film as a collection of many tens of two-dimensional images, also referred to as slices. Each slice is reviewed by the radiologist in the search for abnormalities. Although multiple slices provide more opportunities for a lesion to be detected, the possibility of missing a lesion is also increased due to the increased workload by generating a greater number of individual images per scan. A thoracic CT scan formerly produced approximately 30 sections with the 10-mm collimation that was standard for many years. The same type of scan, with the 1.25-mm collimation available on state-of-the-art multidetector scanners, now generates about 240 section images for radiologists to interpret. With an increase in the number of CT scans being performed for a wide variety of diagnostic and screening purposes compounded by an increasing number of images acquired during each scan, computerized techniques for the automated analysis of CT scans for disease (and especially for lung nodules that may represent lung cancer) are quickly becoming a necessity. CAD systems are now commercially available and are being developed to assist in challenges of detecting lung cancer in chest imagery.
In initial processing steps, CAD systems typically detect many candidate suspicious areas. In subsequent processing, the initial detections are analyzed to determine whether or not to display a detected region to a user in the final stage. Accurate shape estimates of the initial detections are essential to make good decisions regarding whether or not detections are ultimately displayed.
CAD systems are used to assist radiologists in the detection of suspicious lesions. It is essential for CAD systems to have a reliable estimation of the shape of a lesion in order to make accurate decisions regarding whether or not an initial CAD system detection is ultimately displayed to a user. Therefore, there is a need for a method and system for accurate shape estimation of nodules.
Additionally, if a user or CAD system provides a specific cue point about which to estimate a lesion's shape, current methods can be sensitive to the properties of the image at that location. That is, the estimated shape of a lesion can vary significantly as a function of the voxel indicated as a cue point. Voxels are the smallest distinguishable box-shaped parts of three-dimensional images. Because a lesion's shape is a key property used in determining a diagnostic measure of the lesion, a cue-point independent method for estimating the lesion's shape is needed.
Furthermore, in many case, it is of interest to compare two different series of three-dimensional imagery obtained from examinations conducted at different times. In this case, there is a need for imagery from two or more sets to be registered before manual or automated methods may be used to assess temporal changes in a lesion found in both series.